We are at a point in time where advances in technology have enabled production of extremely small, inexpensive, and wirelessly networked sensor clusters. We can thus implant large quantities of sensors into an environment, creating a distributed sensor network. Each individual node in the network can monitor its local space and communicate with other nodes to collaboratively produce a high-level representation of the overall environment. By using distributed sensor networks, we can sculpt the sensor density to cluster around areas of interest, cover large areas, and work more efficiently by filtering local data at the node level before it is transmitted or relayed peer-to-peer [see Meguerdichian, S., et al. Localized algorithms in wireless ad-hoc networks: location discovery and sensor exposure. 2001. Long Beach, Calif., USA: ACM].
Furthermore, by adding autonomous mobility to the nodes, the system becomes more able to dynamically localize around areas of interest allowing it to cover larger total area with fewer nodes by moving nodes away from uninteresting areas. It is well suited to sampling dynamic or poorly modeled phenomena. The addition of locomotion further provides the ability to deploy the sensor network at a distance away from the area of interest, useful in hostile environments. Cooperative micro-robots can reach places and perform tasks that their larger cousins cannot [see Grabowski, R., L. E. Navarro-Serment, and P. K. Khosla, Small is beautiful: an army of small robots. 2003. 289(5): p. 42].
Mobility also allows the design of a system where nodes can seek out power sources, request the dispatch of other nodes to perform tasks that require more sensing capability, seek out repair, and locate data portals from which to report data [see Howard, A., M. J. Mataric, and G. S. Sukhatme, An incremental self-deployment algorithm for mobile sensor networks. 2002. 13(2): p. 113].
But the creation of mobile nodes is not without a price. Locomotion is costly in terms of node size and power consumption. In dense sensor systems, due to the large quantity of nodes and distributed coverage, it is difficult to manually replace batteries or maintain all nodes. Some researchers [see LaMarca, A., et al. Making sensor networks practical with robots. 2002. Zurich, Switzerland: Springer-Verlag] have explored using robots to maintain distributed networks, but this is difficult to implement over large, unrestricted environments. Additionally, the added intelligence and processing power required for a node to successfully navigate in an arbitrary environment further increases the power and size requirements of each node.
Large nodes, in physical size, complexity, cost, and power consumption, prevent the sensor network from being implanted in most environments. [see Sinha, A. and A. Chandrakasan, Dynamic power management in wireless sensor networks. 2001. 18(2): p. 62; see also see Rahimi, M., et al. Studying the feasibility of energy harvesting in a mobile sensor network. 2003. Taipei, Taiwan: IEEE.]
The present invention contemplates a novel type of mobile distributed sensor network that achieves the benefits of mobility without the usual costs of size, power, and complexity. In accordance with a feature of the invention, nodes are employed that harvest their actuation and local navigational intelligence from the environment. The node is equipped with the ability to selectively attach to or embed itself within an external mobile host.
Examples of such hosts include people, animals, vehicles, fluids, forces (e.g. selectively rolling down a hill), and cellular organisms. These hosts provide a source of translational energy, and in the animate cases, they know how to navigate within their environment, allowing the node to simply decide if the host will take it closer to a point of interest. If so, the node will remain attached; when the host begins to take the node farther away from a point of interest, the node will disengage and wait for a new host.
The invention provides a method for combining mobile sensor agents, dense distributed sensor networks, and energy harvesting. The detailed description which follows describes preferred hardware and software systems which combine these mechanisms using a technique here called “parasitic mobility.”
Related Prior Work
Although the invention has no direct precedent, it is inspired by systems in nature and human society and it builds upon current work in the encompassed fields of distributed sensor networks and mobile systems. Wireless sensor networks have become a large area of research, with many universities and institutes contributing. Strategic seed programs begun in the 1990s such as DARPA's SENSIT initiative [see Chee-Yee, C. and S. P. Kumar, Sensor networks: evolution, opportunities, and challenges. 2003. 91(8): p. 1247] have grown into an international research movement. Early work on highly distributed computation and sensor networks at MIT that provides the lineage to this project can be traced back to the Laboratory of Computer Science's Amorphous Computing Group's research in emergent and self-organizing behaviors in computer systems [see Abelson, H., et al., Amorphous computing. 2000. 43(5): p. 74.] This research conducted software simulations that provided a basis for designing distributed, cooperative systems, leading to the Paintable Computing [see Butera, W. J., Programming a Paintable Computer, in Program in Media Arts and Sciences. 2002, Massachusetts Institute of Technology: Cambridge, Mass.] paradigm proposed by Bill Butera of the MIT Media Lab's Object Based Media Group. This platform has progressed from software simulation to the very recent development of hardware implementing a distributed sensor network comprised of about 1000 nodes.
The Responsive Environments Group at the MIT Media Lab designed an earlier versatile sensor network test-bed inspired by the Paintable Computing concept called the Push-Pin computing platform [see Lifton, J., et al. Pushpin computing system overview: a platform for distributed, embedded, ubiquitous sensor networks. 2002. Zurich, Switzerland: Springer-Verlag], which can support over 100 nodes arbitrarily placed atop a 1×1 meter power substrate. Their subsequent interest in electronic skin as an ultra-dense sensor network [see Joseph A. Paradiso, Joshua Lifton, Michael Broxton, Sensate Media: Multimodal Skins as Dense Sensor Networks. BT Technology Journal, 2004] resulted in the creation of the “Tribble” project [see Lifton, J., M. Broxton, and J. A. Paradiso. Distributed sensor networks as sensate skin. 2003. Toronto, Ont., Canada: IEEE.], a large sphere tiled by a hardwired multimodal sensor network. These systems consist of many nodes instrumented with environmental sensors that can communicate with each other to form a global picture of their situation.
All the above projects illustrate many ideas in distributed sensor networks that motivate this research and provide a basis for the design of a system useful in experimenting with the concept of parasitic mobility. The Smart Dust Project at UC Berkeley [see Kahn, J. M., R. H. Katz, and K. S. Pister. Next century challenges: mobile networking for “Smart Dust”. 1999. Seattle, Wash., USA: ACM.] has set a theoretical goal for extremely small nodes in dense embedded sensor networks. While the project itself did not put an actual hardware platform into production, it spun-off into the Mote [see Warneke, B., B. Atwood, and K. S. J. Pister. Smart dust mote forerunners. 2001. Interlaken, Switzerland: IEEE.] and more recently the Spec [see Hill, J., Spec takes the next step toward the vision of true smart dust. 2003. http://wwwjlhlabs.com/jhill_cs/spec/]. The Mote is currently the most popular platform for experimenting with compact wireless sensing. It has also served as a building block for many mobile sensor agent projects, all of which essentially involved putting a Mote onto some sort of robot [see LaMarca, A., et al. Making sensor networks practical with robots. 2002. Zurich, Switzerland: Springer-Verlag.]. The Spec is the current result of a project intended to shrink down the Mote to the theoretical goal of the Smart Dust project. While not yet that small, the Spec is around 4 mm×4 mm (not including the battery or antenna) and will open the door for many dense sensor array experiments.
Similar work is also proceeding at other institutions (e.g. The National Microelectronic Research Center in Cork, Ireland [see Barton, J., et al. Development of distributed sensing systems of autonomous micro-modules. 2003. New Orleans, La., USA: IEEE.]); the research community is congealing around the goal of producing millimeter sized multimodal wireless sensor nodes. Parasitic Mobility is intended as a means to add mobility to systems built to meet the specifications of these projects with regards to size, power, and node complexity; as the nodes grow smaller, parasitic mobility becomes increasingly feasible and desirable. As the power source remains a problem, current research in energy scavenging is very relevant to this initiative [see Thad Starner, Joseph A. Paradiso, Human Generated Power for Mobile Electronics. In C. Piguet (ed), Low Power Electronics, CRC Press, 2004.] and adaptive sensing [see Mohammad Rahim, Richard Pon, William J. Kaiser, Gaurav S. Sukhatme, Deborah Estrin, and Mani Srivastava, Adaptive Sampling for Environmental Robots, in UCLA Center for Embedded Networked Sensing Technical Report 29. 2003, University of California at Los Angeles: Los Angeles, Calif.].
Adaptive sensing is the technique by which sensing capabilities (active sensors, sampling rate, power consumption, bit-depth, transmission, processing) are increased and decreased according to the sensor data itself, never decreasing below a level capable enough to determine when more sensing power is necessary. Such approaches are currently being implemented using the Stack Sensor Platform [see Benbasat, A. Y., S. J. Morris, and J. A. Paradiso. A wireless modular sensor architecture and its application in on-shoe gait analysis. 2003. Toronto, Ont., Canada: IEEE.] at the MIT Media Lab. The Networked InfoMechanical Systems research area at the Center for Embedded Networked Sensing at UCLA conducts research and builds systems to investigate adaptive sensing [see Mohammad Rahim, Richard Pon, William J. Kaiser, Gaurav S. Sukhatme, Deborah Estrin, and Mani Srivastava, Adaptive Sampling for Environmental Robots, in UCLA Center for Embedded Networked Sensing Technical Report 29. 2003, University of California at Los Angeles: Los Angeles, Calif.] and mobility for distributed sensor networks [see William J. Kaiser, Gregory J. Pottie, Mani Srivastava, Gaurav S. Sukhatme, John Villasenor, and Deborah Estrin, Networked Infomechanical Systems (NIMS) for Ambient Intelligence, in UCLA Center for Embedded Networked Sensing Technical Report 31. 2003, University of California at Los Angeles: Los Angeles, Calif.].
The MIT Laboratory for Computer Science's Network and Mobile Systems group has conducted substantial research in wireless and sensor networks. Several of their projects are directly related to the work of this thesis. They include a protocol for networking Bluetooth nodes [see Law, C., A. K. Mehta, and K. Y. Siu, A new Bluetooth scatternet formation protocol. 2003. 8(5): p. 485.] and the LEACH protocol for sensor networking [see Heinzelman, W. R., A. Chandrakasan, and H. Balakrishnan. Energy-efficient communication protocol for wireless microsensor networks. 2000. Maui, Hi., USA: IEEE Comput. Soc.]. These are examples of self-configuring network protocols that support mobile nodes of any variety including parasitically actuated.
And finally, while not distributed sensor networks, there are several mobile sensor devices built by attaching large sensor packages to floating platforms that drift about in ambient flows while collecting data. Some examples include Sonobuoys [see Houston, Kenneth M.; Engebretson, Kent R., The Intelligent Sonobuoy System—A Concept for Mapping of Target Fields, in Draper Laboratories, D. Laboratories, Editor.] that acoustically hunt for submarines, drifting instrumentation packages to monitor ocean temperature [see James D. Irish, Walter Paul, J. N. Shaumeyer, Carl C. Gaither, III, and John M. Borden, The Next-Generation Ocean Observing Buoy in Support of NASA?s Earth Science Enterprise. Sea Technology, 1999 (40): p. 37-43], and balloon-borne modules for surveillance and proposed planetary exploration [see Kerzhanovich, V. V., J. A. Cutts, and J. L. Hall. Low-cost balloon missions to mars and venus. 2003].
Parasitic Mobility in Nature
The natural world provides us with many examples of parasitic mobility, including organisms that rely entirely on larger organisms to carry them to habitable locations. Parasitic relationships of this sort are called phoretic relationships from the word phoresis, which literally means transmission [see Albert O. Bush, Jacqueline C. Fernandez, Gerald W. Esch, J. Richard Seed, Parasitism: The Diversity and Ecology of Animal Parasites. 2001, Cambridge University Press: Cambridge, UK. p. 391-399]. These examples may be separated into three categories:    (1) active parasitic mobility consisting of organisms that attach and detach at will from hosts with their own actuation,    (2) passive parasitic mobility consisting of passive nodes that are picked up and dropped off, knowingly or unknowingly, by hosts, and    (3) value-added parasitic mobility which consists of either passive or active parasitic organisms that provide additional value to the host in exchange for transportation.Each of these categories of parasitic mobility will be discussed briefly below:
Active Parasitic Mobility
The first example that comes to mind when discussing parasites in nature is the tick. The tick actively attaches to hosts by falling from trees or by crawling directly onto the host. It remains attached by using an actuated gripping mechanism which it can release whenever it decides to seek food elsewhere. Although the tick is transported to new locations by the host, its primary reason for attachment is to use the host as a source of food. It is therefore not normally considered a phoretic organism, but is a leading example of an active attachment mechanism.
Several species of nematodes, a.k.a. round worms, exhibit phoretic behaviors. The Pelodera Coarctata is a nematode that is commonly found living in cow dung. When the conditions in the dung deteriorate and become inhospitable for the nematode, it attaches itself to a dung beetle which will carry it to a new fresh dung pat. [see Albert O. Bush, Jacqueline C. Fernandez, Gerald W. Esch, J. Richard Seed, Parasitism: The Diversity and Ecology of Animal Parasites. 2001, Cambridge University Press: Cambridge, UK. p. 160-196.] Another such nematode is the Onchocerca Volvulus which is infamous as the cause of “River Blindness.” This worm attaches itself to Blackflies that in turn bite humans allowing the worm to travel through the skin and infect the host human. These Blackflies themselves are also an example of parasitic mobility. Their larvae require an aquatic stage for growth, so they often attach themselves to freshwater crabs to bring them into the water and protect them. [see Albert O. Bush, Jacqueline C. Fernandez, Gerald W. Esch, J. Richard Seed, Parasitism: The Diversity and Ecology of Animal Parasites. 2001, Cambridge University Press: Cambridge, UK. p. 160-196.]
Marine life is ripe with examples of active parasitic mobility. One example is that of the Remora or Suckerfish. These fish have developed a sucker-like organ that they use to attach to larger creatures such as sharks or manta rays. By attaching to these larger, faster animals the remora covers area faster giving it more access to food. [see Albert O. Bush, Jacqueline C. Fernandez, Gerald W. Esch, J. Richard Seed, Parasitism: The Diversity and Ecology of Animal Parasites. 2001, Cambridge University Press: Cambridge, UK. p. 306-310.]
Passive Parasitic Mobility
Plants often employ parasitic mobility as a means of distributing seeds. A common example of this is the dandelion. The dandelion seeds have a tiny parachute that carries the seed with the wind. This allows the seeds to travel some distance in hopes of landing in an area that provides the requirements of growth. It is completely passive and at the whim of the wind. It is not expected that all the seeds will land in arable areas. This is overcome by the sheer quantity of seeds released into the air. This is more opportunistic than parasitic, but still falls within the conceptual boundaries of this research. Other plants, with behaviors more aptly described as parasitic, distribute their seeds in bur casings. These prickly cases stick to animals that brush up against them or step on them. They are shaken lose or fall off as a result of shedding, usually at a new location.
Value-Added Parasitic Mobility
Fruit-bearing trees distribute their seeds in a value-added method. Animals gather the fruits as a food source and in turn spread the discarded seed-containing cores. This attraction and provision acts as an attachment mechanism for the seeds. The detachment mechanism is the inedibility of the seeds within the fruit, in other words, when the added value has been used up. Flowers use their scented petals to attract bees and other insects. The flowers also provide nectar. The bees use the nectar to make honey and carry the pollen from flower to flower. This is an extremely well evolved symbiotic system that has very little wasted energy or resources. [see Albert O. Bush, Jacqueline C. Fernandez, Gerald W. Esch, J. Richard Seed, Parasitism: The Diversity and Ecology of Animal Parasites. 2001, Cambridge University Press: Cambridge, UK. p. 6-9]. The existence of many such well evolved systems in nature illustrates the validity of this type of mobility.
Parasitic Mobility in Society
In human society, many of the systems surrounding us exhibit emergent behaviors that exemplify parasitic mobility. It is important to examine these systems, not only as conceptual examples, but also because it may be possible to embed sensor network technology directly into these existing systems and take advantage of their mobility. Basic examples, such as people being pulled along by a bus, exist throughout society. It is often beneficial to attach to something that can travel in ways that a person cannot. The example further illustrates the economies of parasitic mobility, as the people are getting a free ride.
A simple example of parasitic mobility is when a lost object, such as a cellular phone, is returned to its owner. This method of actuation is a combination of the device identifying its destination and a desire for the host to bring it there. Keeping this in mind, it may be possible to design devices that could identify some sort of reward for bringing them to a point of interest to the device. Another example of this behavior is that of a consumer survey (a sensor of sorts) that is redeemable as a coupon when returned. There are many everyday objects that are only useful for short bursts. One example of this is a writing utensil. A pen is needed to record information when it is presented or invented; afterwards the pen sits dormant awaiting the next burst of usefulness. During this period where the pen is not deemed useful it is free to be relocated. It is often relocated by a host requiring its use in another location. As a result, pens generally cover large areas over time, and due to their unlikelihood of being returned, people usually have redundant supplies of pens. Equipping pens with a sensor device is a good way to gain coverage of an environment, particularly an office or academic institutional building.
Fictional Examples of Parasitic Mobility
Some popular works of fiction have played with a few ideas that relate to parasitic mobility. The concept of people hanging onto vehicles for free rides has been taken further by Neal Stevenson in his novel Snow Crash [see Stevenson, N., Snow Crash. 1992: Bantam Books]. In Snow Crash, hitching rides on other vehicles via futuristic skateboards equipped with magnetic grapplers to latch onto cars is presented as a major method of transportation in the future setting of the story. In the movie Twister [see de Bont, Jan, Twister. 1996, Warner Bros.: USA]. a team of storm-chasers release a batch of sensors into a tornado. The sensors, collectively called ‘Dorothy’, are sucked up into the vortex and collect data about the tornado from the inside. These sensor nodes are carried into the area of interest by winds themselves. In this case the sensor nodes are used to study the actuation force itself, and is mobile along with the force thereby always being at the area of interest. Although it seems possible that this system can be deployed, according to the National Severe Storms Laboratory [see The National Severe Storm Laboratory, FAQ. http://www.nssl.noaa.gov/faq/vortex.shtml], such devices have not been built. They have experimented with a large barrel-sized sensor device called TOTO (TOtable Tornado Observatory), but these tests have yielded only minimal success.
Finally, the most famous example of an object that travels without its own actuation is ‘The One Ring’ from the “Lord of the Rings Trilogy.” [see Tolkien, J. R. R., The Lord of the Rings. 1954-1955, London: George Allen & Unwin]. This ring calls out to potential hosts to pick it up, and even renders the wearer invisible as a value-added service. And finally, the ring desires to be brought to a location which also happens to be the only place it can be destroyed; a promised reward for its successful journey.